{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib——简单图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 画图——基础"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HevueKtnYazmjnoaacFhaz3AyrqV6exjRNnbNqIvUj3p7GFGmGM2oi9SbenuiMo1dM+oi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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114108190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "x = [1,2,3,1]\n",
    "y = [1,3,0,1]\n",
    "\n",
    "plt.plot(x,y)\n",
    "\n",
    "#plt.title('Triangle')\n",
    "#plt.ylabel('Y axis')\n",
    "#plt.xlabel('X axis')\n",
    "\n",
    "#plt.xticks([1,6])\n",
    "#plt.yticks([1,6])\n",
    "#plt.xticks([1,6],['a','b'])\n",
    "\n",
    "#plt.ylim([-1,4])\n",
    "#plt.xlim([-1,4])\n",
    "\n",
    "plt.grid(True,color='r')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 画图——样式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "marker类型参考 http://www.labri.fr/perso/nrougier/teaching/matplotlib/#line-properties\n",
    "\n",
    "    颜色缩写    全称\n",
    "    b          blue\n",
    "    c          cyan\n",
    "    g          gree\n",
    "    k          black\n",
    "    m          magenta\n",
    "    r          red\n",
    "    w          white\n",
    "    y          yellow\n",
    "   \n",
    "   \n",
    "    线型缩写    含义\n",
    "    --         --虚线\n",
    "    -.         -.虚线\n",
    "    :          .虚线\n",
    "    -          实线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YTr3gepy+cpqXVr6kr7xS2pw2lM763b5MQkIC33zzDW3atKFChQq59tntdn78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\nHR8/fLhWaGvxYkhK8p5ON2AFnVbQCEq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1137e43d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = [1,2,3,1]\n",
    "y = [1,3,0,1]\n",
    "\n",
    "# np.array将列表转换成numpy的数组后可以支持广播broadcast运算\n",
    "plt.plot(x,y,color='r',linewidth='2',linestyle='--',marker='D', label='one')\n",
    "plt.plot(np.array(x)+1,np.array(y)+1,color='g',linewidth='3',linestyle=':',marker='o', label='two')\n",
    "plt.plot(np.array(x)+2,np.array(y)+3,color='k',linewidth='1',linestyle='-.',marker='>', label='three')\n",
    "\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "#plt.legend(loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 画图——中文"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 下载参考资料里的微软雅黑字体msyh.tff\n",
    "2. 将字体拷贝到matplotlib安装位置 /matplotlib/mpl-data/fonts/\n",
    "3. 修改配置文件 /matplotlib/mpl-data/matplotlibrc\n",
    "\n",
    "    1) backend      : TkAgg  #mac需要修改，windows默认就是TkAgg\n",
    "    \n",
    "    2) font.family         : Microsoft YaHei\n",
    "    \n",
    "    3) font.serif          : Microsoft YaHei, ......#(后面的不变，只在前面加雅黑字体)\n",
    "    \n",
    "    OK, 大功告成，试试效果吧"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oGCReVC81TlrDopp6qenDQcEgcaR6KR7SFBaV1ktNHQ4KBokz1Uvxk/SwqKRe\natpwUDBI3Kleir+khUUl9VJThoOCQZJC9VKyJCEsyq2Xmi4cFAySNKqXkiuuYVFOvdRU4aBgkCRS\nvZQecQmLcuqlpgkHBYMkmeqldGpkWHRWL3Xv2jX94fDXTX9VMEjiqV5Kv6jDoqN66UdDh8Y/HMys\nJzDY3VdU8Vo/5s5jFAySeKqXmk+9w+Kgfv0446WXWPXpp9s8p4cZG9ra4hsOZtYXuBWYAPybu59R\nMH8UcDvQH7jX3c8tsQxnRollKxgkgVQvNbd6hEW7jjiiqnCI6tzvLcAc4Px25l8HXAiMAEab2THl\nLFTBIEm1S59duPboa4seX7tuLdMemNaAEUmUupgxqqWFabvtxoJRo3jzkEP4wxe+wLV77ME3d9yR\ngd27N3qI0YSDu3/i7o8BmwvnmdlAYJi7PxQcjnQ7cFQ5y52yzxSO2/u4cAcrEpET9jmBSXtNKnp8\nwQsLWLBsQQNGJI0Sx7CIep/DKcD4/FrJzMYAP3X3w4Lpo4Ez3P3rBa8tWSt1ta6M+9w42oa10Tas\njfGDx9N3u771/BgioVG9JOWoqYaqslaKQzgcCMxy98OD6a8Ap7v7Nwte6xye98AwYHjxeygsJGl0\n9JJUqsOwWLo0+5Uzb15iw2EwkHH33YPp04B93P38gteW3HLojMJC4s7d+cb8b7DwjwuL5unoJSlH\nh2GRoC2HQ9399ILHnwXOBp4CHgEudvdFBc+pKhwKKSwkjlQvSZhyYfGfH37Idz/3ufiGg5n1AZYA\nfYCewFrgAmB3d59tZmOBeWQPZZ3r7jNKLMPdnVXvr+LxVY+TeTVDZlWGVe+vqmlsCguJC9VLUg9N\nc/mMQgoLSQvVS1IPTRsOhRQWkmSqlyRsCod2KCwkaVQvSZgUDmVSWEjcqV6SMCkcqqSwkDhSvSRh\nUTiERGEhcaF6ScKgcKiTeoTFAZ87gLah2bA4dMihCgspSfWShEHhEBGFhURJ9ZLUSuHQIAoLqTfV\nS1ILhUNMKCwkbKqXpBYKh5hSWEgYVC9JtRQOCaGwkGqpXpJqKBwSSmEh5VK9JNVQOKSEwkI6onpJ\nKqVwSCmFhRRSvSSVUDg0CYWFqF6SSigcmpTCojmpXpJyKRwEUFg0E9VLUg6Fg5SksEgv1UtSjtiH\ng5kdD1wFbAJ+4u5z8+bNBY4E1gMOTHD3NQWvVziEQGGRLqqXpDOxDgcz6wO8ABxI9pf/UmAfd38n\nmD8XuNlHxgxOAAAISklEQVTdn+hgGQqHOlBYJJ/qJelI3MPhG8Cx7v53wfQvgXvdfX4wPRe4xd0f\n72AZCocIKCySR/WSdCTu4XAeMMDdLw2m/wl43d2vCaZvJFsrfQzMdffZJZahcGgAhUUyqF6S9sQ9\nHC4EWtx9ejD9E+DP7n5twfMGAQ8D09z90YJ5CocYUFjEl+olKSXu4XAy0ObupwXTtwF3ufs9JZ47\nC1jt7nMKHvfp06dvnW5ra6Otra2u45bOKSziQ/WSAGQyGTKZzNbpmTNnxjocdgYWA2OBbsCTwL7u\nvj6Yv7u7v2JmA4AMcIa7P12wDG05JIDCorFUL0mhWG85AJjZ3wGXkT1a6YeAASPcfbaZ3Q+MBD4F\n5rj79SVer3BIIIVF9FQvSb7Yh0OtFA7poLCoP9VLkk/hIImksKgP1UuSo3CQVFBYhEf1koDCQVJK\nYVE91UsCCgdpEgqLyqheEoWDNCWFRedULzU3hYMICotSVC81N4WDSAn1DovxQ8bTb7t+4Qy2jlQv\nNS+Fg0gZmjksVC81J4WDSBWaKSxULzUnhYNICNIeFqqXmo/CQaQO0hgWqpeai8JBJAJpCAvVS81F\n4SDSAEkNC9VLzUPhIBIDSQoL1UvNQeEgEkNxDgvVS81B4SCSAHELC9VL6adwEEmgOISF6qV0UziI\npEAjwkL1UropHERSKKqwUL2UXrEPBzM7HrgK2AT8xN3n5s0bBdwO9AfudfdzS7xe4SBNr55h8da6\nt7hl6S1Fz1G9lGyxDgcz6wO8ABwIOLAU2Mfd3wnmPw78I/Aw8Bgw293vLVhGqsMhk8nQ1tbW6GHU\njT5ffYQdFu2ZPmQ6M74zoy7LbrS0/7dZbTh0qcdgSvgKkHH3N9z9TeAR4MsAZjYQGObuDwW//W8H\njopoXLGRyWQaPYS60uerj2GtwzhlzCnMPXYuK89dycpzV3LLsbcwdcxUhrUOC+19Zt0xi7WfrA1t\neXGS9v82q9UtovcZDLyaN/1nYNfg50HAa3nz1gD/M6JxiaTKsNZhDBuTDQwIb8ti3cZ1XPLoJdw4\n8cYQRytxFlU49AC25E1vATaXMU9EahBmWCz840J+9rWfYVZxQyEJFNU+h5OBNnc/LZi+DbjL3e8x\ns8FkK6fdg3mnkd0fcX7BMtK7w0FEpI7ivEN6Z2AxMJbs1sqTwL7uvj6Y/yxwNvAU2f0RF7v7oroP\nTERESoryUNa/Ay4je7TSDwEDRrj7bDMbC8wjeyjrXHefEcmgRESkpMScBCciItGJ6lDWiplZTzPb\no9HjqJe0fz4RSbbYhYOZ9TWzhcCbwAUl5o8ys6VmttLMrol+hLUp4/PNNbM1ZrbCzF4ys0HRj7I6\nZradmf3MzJYH6+e8gvlJX3edfb7ErjsAy3oo+HwvmtmRBfOTvv46+3yJXn8AZtbdzJaZ2Y0Fj1e+\n7tw9Vl9AC3AEcCpwY4n5jwN/S3afRQY4ptFjDvnzzQUOa/Q4q/xsOwBfD34eALwB7JaiddfZ50vs\nusv7DDsH378C/HfBvESvvzI+XxrW33TgwcLfLdWsu9htObj7J+7+GCXOdUjD2dQdfb48sVsv5XD3\nd919YfDzO8BqoBVSs+7a/Xx5Ernucjx7BQOAoWQvcwOkY/1B+58vT2LXn5ntDRwE3FHweFXrLmn/\nEKXOpt61necm1UbgFjN73sy+3+jBVMvM9gW6u/uy4KFUrbsSnw9SsO7M7AIzexs4D7g8b1Yq1l8H\nnw+Sv/7mAOeQ3TrIV9W6S1o4pP5sanc/w92HA0cDp5vZhEaPqVJmthPZQ5NPyXs4Neuunc+XinXn\n7rPcfSBwCfBQ3qxUrL8OPl+i15+ZnQk85u4vl5hd1bpLWjj8hWwK5gwiu2mfOu6+BrgP2KfRY6mE\nme0A3Atc4O7P5s1Kxbrr4PNtldR1ly+oz/oEnxdSsv5ySny+/HlJXH/fBqaY2RKyW0RfN7MfBPOq\nWndxD4dtNo/cfTXwsZl9ycy6AicDCxoysnAUndJuZrnLiAwg2wv+d9SDqpaZ9Sf7i3O6uz+SPy8N\n666jzxfMT+y6AzCz4cHVDDCzLwLr3f1dSM36a/fzBY8ldv25+3h3H+3uY8mebLzQ3a8O5lW17qK6\n8F7ZLHvvhyVAH6CnmR1O9pDP3d19NjCVbc+mTtRlNsr4fHPMbCTwKTDH3Z9u3GgrdjYwGvipmRnZ\ns+GvJ3uyZeLXHZ1/viSvO8juXH/QzLqQPdR6iplNIriSAclff519vqSvv23Uuu50hrSIiBSJe60k\nIiINoHAQEZEiCgcRESmicBARkSIKBxERKaJwEBGRIgoHEREponAQqZGZ9Wv0GETCpnAQKYOZfcfM\nfpc3vaeZ/dnMhgAPmNkAM+ueN//XDRmoSEh0hrRImczsv4B/cvdfmdk9wF3ARGAT2XsDLAUuDZ6+\nN/AC8Bd3n9KI8YrUQlsOIuU7D/jH4PaSO7j7bcAHwNPAMmA58Ly7HwYscvcvAaqcJJEUDiJlCi7E\nthi4E5gWXHxvMPAu2QspiqRG7K7KKhJzLWTvGNaL7HXxxwB/Q/YKrRfmPa/ocuwiSaJwECmTmR0L\nDAS+A1zn7gcAu5jZt8jud3gGuNbM9gL2NbPHgB0bNmCRGqhWEilDcB+OfwHOdvcHgbfM7JzgBjF/\nAxwGfB64x90nAE+7+xFse+9ekcTQloNIeX4MPJh3a9AfAr8H9gR6kr3t4ht8VicVfhdJFB3KKlKj\nvFrpv4BFZI9agmww7OjuSboXsQigcBCpmZn1AHD3DY0ei0hYFA4iIlJEO6RFRKSIwkFERIooHERE\npIjCQUREiigcRESkiMJBRESKKBxERKTI/wfcsz1nBoc93gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112d73850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = [1,2,3,1]\n",
    "y = [1,3,0,1]\n",
    "\n",
    "x2 = np.array(x)+1\n",
    "y2 = np.array(y)+1\n",
    "\n",
    "plt.plot(x,y,'g',label=u'第一个', linewidth=5)\n",
    "plt.plot(x2,y2,'c',label=u'第二个',linewidth=5)\n",
    "\n",
    "plt.title(u'两个三角形')\n",
    "plt.ylabel(u'Y轴')\n",
    "plt.xlabel(u'X轴')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 画图——公式TeX支持"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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goPayzMxMfv/739OtWzfuvvtu+vTpQ/fu3Tlw4ICvSxMH+eijj2jRogWbNxd9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\nCmoREYdTUIuIOJyCWkTE4RTUIiIO9/+V0UYOwVeRtwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130d9f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "ax= fig.add_subplot(111)\n",
    "ax.set_xlim([1, 6]);\n",
    "ax.set_ylim([1, 9]);\n",
    "ax.text(2, 8, r\"$ \\mu \\alpha \\tau \\pi \\lambda \\omega \\tau \\\n",
    "    lambda \\iota \\beta $\");\n",
    "ax.text(2, 6, r\"$ \\lim_{x \\rightarrow 0} \\frac{1}{x} $\");\n",
    "ax.text(2, 4, r\"$ a \\ \\leq \\ b \\ \\leq \\ c \\ \\Rightarrow \\ a \\\n",
    "    \\leq \\ c$\");\n",
    "ax.text(2, 2, r\"$ \\sum_{i=1}^{\\infty}\\ x_i^2$\");\n",
    "ax.text(4, 8, r\"$ \\sin(0) = \\cos(\\frac{\\pi}{2})$\");\n",
    "ax.text(4, 6, r\"$ \\sqrt[3]{x} = \\sqrt{y}$\");\n",
    "ax.text(4, 4, r\"$ \\neg (a \\wedge b) \\Leftrightarrow \\neg a \\\n",
    "    \\vee \\neg b$\");\n",
    "ax.text(4, 2, r\"$ \\int_a^b f(x)dx$\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Annotate各种格式可参考文档\n",
    "\n",
    "http://matplotlib.org/users/annotations_guide.html#annotating-with-arrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11531a3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(1, figsize=(3,3))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "ann = ax.annotate(\"Test\",\n",
    "                  xy=(0.2, 0.2), \n",
    "                  xytext=(0.8, 0.8), \n",
    "                  size=20, va=\"center\", ha=\"center\",\n",
    "                  bbox=dict(boxstyle=\"round4\", fc=\"w\"),\n",
    "                  arrowprops=dict(arrowstyle=\"-|>\",\n",
    "                                  connectionstyle=\"arc3,rad=0.2\",\n",
    "                                  fc=\"r\"), \n",
    "                  )\n",
    "ax.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 画图——多子图结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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C4ydawU84fwIiEteCn3D+BKfgRzZBQ6uCb2lpSThOE60yH3rU0JSVeXAleCaFWCxXj1Hw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\nSj4QkUOBx4BJuZSrqjNUtQ8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GQeKHqyelcM7oQiyq2mYhFhEZBtThErndoaqvx2nTwjmN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\n8RuGYZQZ/x+NSr+TZvPCpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113363a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# arange类似python里的range\n",
    "t = np.arange(0., 5., 0.2)\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(221) \n",
    "ax2 = fig.add_subplot(222)\n",
    "ax3 = fig.add_subplot(212)\n",
    "\n",
    "ax1.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n",
    "ax1.grid(True)\n",
    "\n",
    "ax2.semilogy(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n",
    "ax2.grid(True)\n",
    "ax2.set_title('ylog')\n",
    "\n",
    "ax3.loglog(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n",
    "ax3.grid(True)\n",
    "ax3.set_title('loglog')\n",
    "\n",
    "fig.suptitle('normal vs ylog vs loglog')\n",
    "#fig.subplots_adjust(hspace=0.4)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
